Degree of shape deformation in the right NAcc was associated with higher scores of the STAI-Trait, and STAI-State measures in the PD patients. Limitations All the patients received medication such as Psychotropic drug. Conclusion Patients with PD showed reduced volumes in the NAcc, especially in lateral regions, compared with HC. Furthermore, shape deformation in the right NAcc was associated with trait anxiety and state anxiety, which has been associated with avoidance behavior.Background Loneliness is common among left-behind adolescents and can affect their friendship quality both negatively and positively. Most studies have focused on the negative effect of loneliness on friendship. However, loneliness may also motivate adolescents to seek close connections with peers through certain ways, that is, loneliness may indirectly positively influence friendship quality, which has not been explored. To address this gap, based on the life history theory and the interpersonal function model of non-suicidal injury (NSSI), this study aimed to examine the positive impact of loneliness on friendship quality via NSSI among left-behind adolescents. Moreover, given that NSSI is a severe health concern that should be prevented, the protective role of parent-child cohesion was also examined. Methods A two-wave dataset was used. Participants comprised 1,013 adolescents (539 left-behind and 474 non-left-behind adolescents) completed self-report surveys that addressed loneliness, NSSI, friendship quality, and parent-child cohesion. Results For left-behind adolescents, loneliness could affect friendship quality not only negatively but also positively through NSSI; increased loneliness predicted more NSSI which, in turn, was associated with high friendship quality. Moreover, the moderating effect of parent-child cohesion was significant. For non-left-behind adolescents, neither the direct nor the indirect positive effect through NSSI between loneliness and friendship quality was found. Limitations All measures were based on self-reports. Cohesions with caregivers were not included. Conclusions Findings advance our understanding of the relationships between loneliness, NSSI, and friendship quality among left-behind adolescents. They provide important implications for future interventions by addressing the role of high parent-child cohesion.Background Although several neuropsychiatric symptoms (NPSs) have been demonstrated to have value in the prediction of the progression of mild cognitive impairment (MCI) to dementia, these symptoms are less studied for the prediction of the transition from normal cognition (NC) to MCI. https://www.selleckchem.com/products/geneticin-g418-sulfate.html Methods Prospective cohort studies were included if they reported on at least one NPS at baseline and had MCI as the outcome. Results We obtained 13 cohort studies with a total population of 33,066. Depression was the most common neuropsychiatric symptom and could significantly predict transition to MCI (RR = 1.49, 95% CI 1.13-1.86). However, depression was more capable of predicting amnestic MCI (RR=1.43, 95% CI 1.04-1.83) than non-aMCI (RR= 0.96, 95% CI 95% CI 0.60-1.33). Subgroup analysis suggested that the association between depression and MCI changed with depression severity, depression criteria, apolipoprotein-E-adjusted status, age, the percentage of females, and follow-up times, but some data were too sparse for a reliable estimate. Regarding other NPSs, there were insufficient data to assess their effect on the development of MCI. However, apathy, anxiety, sleep disturbances, irritability, and agitation might be risk factors for the prediction of NC-MCI transition with strong predictive value. Conclusions Depression was associated with an approximately 1.5-fold sincreased risk of the progression to MCI in the population with normal cognition. Other NPSs with underlying predictive value deserve more attention.Introduction Perinatal and later postnatal adversity (e.g., child sexual abuse) are predictors of psychopathology across the lifespan. However, little is known about the impact of the joint effects of perinatal and postnatal adversity on the longitudinal trajectories of mental health problems from adolescence through adulthood. Method We utilized data from a prospective, longitudinal birth cohort of extremely low birth weight (ELBW; 2500 g) control participants. Self-report data on internalizing (depression, anxiety) and externalizing (antisocial) problems were collected at 12-16, 22-26, and 30-35 years of age. Results A birth weight by child sexual abuse (CSA) interaction was observed such that ELBW survivors exposed to CSA had higher levels of internalizing problems from adolescence through adulthood than NBW participants exposed to CSA. Differences remained significant after adjustment for covariates. Likewise, ELBW survivors exposed to CSA had higher levels of internalizing problems from adolescence through adulthood than ELBW participants who were not exposed to CSA. Limitations Findings are limited by sample attrition due to the longitudinal nature of the study spanning over 30 years as well as the retrospective nature of child sexual abuse reporting. Conclusions Exposure to both perinatal and later postnatal adversity leads to persistently higher internalizing problems than exposure to either adversity alone over more than two decades. These findings suggest that individuals exposed to perinatal adversity may be especially vulnerable to, and persistently affected by, childhood adversity, particularly in the form of depression and anxiety.Background The predictive accuracy of suicidal behaviour has not improved over the last decades. We aimed to explore the potential of machine learning to predict future suicidal behaviour using population-based longitudinal data. Method Baseline risk data assessed within the Scottish wellbeing study, in which 3508 young adults (18-34 years) completed a battery of psychological measures, were used to predict both suicide ideation and suicide attempts at one-year follow-up. The performance of the following algorithms was compared regular logistic regression, K-nearest neighbors, classification tree, random forests, gradient boosting and support vector machine. Results At one year follow up, 2428 respondents (71%) finished the second assessment. 336 respondents (14%) reported suicide ideation between baseline and follow up, and 50 (2%) reported a suicide attempt. All performance metrics were highly similar across methods. The random forest algorithm was the best algorithm to predict suicide ideation (AUC 0.83, PPV 0.